诊断试验和筛检试验

1、第6章 诊断和筛检试验(2h)CHAPTER 6 Assessing the Validity and Reliability of Diagnostic and Screening Test (2h)第一节 概述筛检与筛检试验筛检：运用快速、简便的检验手段，在健康的人群中，发现潜在的可以病人或缺陷者。试验方法多样。其目的是区分健康和可疑者。无诊断之作用。诊断和诊断试验：P97 当前重大的公共卫生问题 阳性者有进一步确诊手段 确诊者有明确有效的治疗办法 自然是明确，可被发现 方法简便安全筛检试验应具备的条件诊断的应用原则灵敏、特异安全无痛苦价廉The aim of screening is t

2、o detect a chronic disease at its asymptomatic stage, so that early cases can be treated early with a hope for better prognosis. Types of screening may include mass screening, selective screening, single-disease screening, multiple-disease screening, and case finding. The validity or accuracy of scr

3、eening tests can be measured by sensitivity, specificity, and likelihood ratios. The reliability of screening tests is affected by subject variability, observer variability and laboratory conditions. Positive and negative predictive values which are determined by the test accuracy and the pre-test p

4、robability in the screened population are used to make sense of the test results. Screening programs can be evaluated in its biological, clinical and economic benefits at either an individual or population level or both. Lead time bias, length bias and volunteer bias are common bias in such evaluati

5、ons and should be particularly avoided.开始暴露出现症状临床前可检查期 疾病发生诊断治疗筛检康 复残疾、死亡 易感期 临床前期 临床期 筛检试验与诊断试验的分别筛检试验诊断试验对象健康人或无症状的病人病人目的发现可疑病人对病人进行确诊要求快速、简便、安全，高灵敏度 复杂、准确性和特异度高 费用经济、廉价花费较高 处理用诊断试验确诊 严密观察和及时治疗 实施原则社会学方面 重大公共卫生问题对筛检阳性者能实行有效的追踪和干预有比较高的成本-效益比所用筛检技术易于被群众接受科学方面 对所筛检疾病的自然史有比较清楚的了解所筛检疾病有可识别的早期临床症状或体征所筛检

6、疾病有足够长的领先时间对筛检疾病疾病的预防效果及其副作用有清楚认识所筛检疾病疾病有比较高的流行率伦理学方面 已确认筛检可以改变疾病的自然史有相应的诊断和治疗方法有可行的预防措施 筛检方法 确诊方法 有效的治疗手段 三者缺一不可，否则将导致卫生资源浪费，给筛检试验阳性者带来生理和心理上的伤害等不良后果 基本条件伦理学问题 个人意愿 有益无害 公正平等第二节 评价确立金标准：相对而言选择研究对象：包括病例组和非病例组，要求代表性要好确定样本含量：P99真实性评价：灵敏度、假阳性率、特异度、假阴性率、约登指数、粗一致性等可靠性评价：变异度、Kappa值等收益评价：预测值、似然比确定截断值：避免偏倚V

7、alidity（真实性） of screening testValidity has two components: sensitivity and specificity.Sensitivity-defined as the ability of the test to identify correctly those who have the disease.Specificity-defined as the ability of the test to identify correctly those who do not have the disease.For example-Su

8、ppose we have a hypothetical population of 1000 people, of whom 100 have a certain disease and 900 do not. A test available that can yield either positive or negative results. We want to use this test to try to separate persons who have the disease from those who do not.Screening test to identify th

9、e 100 people with the diseaseSensitivity=80/100=80%specificity=800/900=89%Result of testDisease No diseaseTotal Positive80100180Negative 20800820Total 1009001000假阴性率、假阳性率约登指数（Youdens index）-正确指数粗一致性Gold standardWhen we calculate the sensitivity and specificity, we must know who “really” has the dise

10、ase, that is we are comparing our test results with the “gold standard”-”truth”.Sometimes this truth may be the result of another test that has been in use, or more definitive, more invasive test. (like tissue biopsy or cardiac catheterization)Once the truth be defined, it will be used to compare wi

11、th another test.Ideally, we would like all of the tested subjects to fall into the two cells, TP and TN.Sensitivity=TP/TP+FNspecificity=TN/TN+FPResult of testDisease No diseasePositiveTrue positive (TP)=have disease and have positive testFalse positive (FP)=no disease but have positive testNegative

12、False negative (FN)=have disease but have negative testTrue negative (TN)=no disease and have negative testIn the 4 cells, we care more about FP and FN. We wish both them are 0, all individuals have disease should be tested positive, no disease be tested negative.If a health person be tested “positi

13、ve”, he need more examination; if a patient be tested “negative”, like cancer which is curable only in its early stages, what will happen?How the cut-off level select?Depends on the relative importance of false positivity and false negativity for the disease in question.Biologic variation of human p

14、opulationsBimodal curve, one is normal, the other is abnormal. But sometimes there are overlapped, so it is important to define the “cut point”.Most of population can be easily distinguished using the two curves, if some individuals fall into the “grey zone”, 病人与非病人观测值分布类型受试者特征工作曲线P108Reliability（可靠

15、性） of testThat is another aspect of assessing diagnostic and screening test, as we always asked: can the results obtained be replicated if the test is repeated?Clearly, regardless of the sensitivity and specificity of a test, if the results cannot be reproduced, the value and the usefulness of the t

16、est are minimal.The factors that contribute to the variation are: intra-subject variation and inter-observer variation.Intrasubject variationThe value obtained in measuring many human characteristics often vary over time, even during a short period. For example, blood pressure.Interobserver variatio

17、nTwo examiner often do not derive the same result, cause the variation.We can use percent agreement to describe.For example-Reading No.2Reading No.1 AbnormalSuspectDoubtfulNormalAbnormalABCDSuspectEFGHDoubtfulIJKLNormalMNOPPercent agreement=(A+F+K+P)/total readingIn the example we can see, A, F, K,

18、P are same result in two reading. Thats we most care. In general, most persons who are tested have negative. There is likely to be considerable agreement between the two observers regarding these negative, or normal, subjects. Therefore, when percent agreement is calculated for all study subjects ,

19、its value may be high only because of the large number of negative findings on which the observers agree. So -Another example-PositiveNegativePositiveabNegativecdPercent agreement=a/(a+b+c)observer1observer2ignoreKappa值取值范围-1到1：-1=完全不一致；0=一致完全由机遇造成；1=完全一致；观察一致性=（a+d）/n机遇一致性=(r1c1/n+r2c2/n)/n非机遇一致性=1

20、-机遇一致性实际一致性=观察一致性-机遇一致性Kappa=实际一致性/非机遇一致性Relation between validity and reliability The test result is quite reliable (repertable), the curve is narrow , variation is small,but not valid.Test resultTrue valueRelation between validity and reliability The test result is valid, but not reliable, because

21、 the curve is broad, variation is big. Test resultTrue valueRelation between validity and reliability The tests valid is good and reliable.Test resultTrue valuePredictive value（预测值） of a testIn clinical setting, we wonder: if the test results are positive, what is the probability that this patient h

22、as the disease? This is called the “positive predictive value of the test”. To do so, we must divided the number of true positive by the total number who tested positive (true positive + false positive)Same question can be asked in negative test, so its called negative predictive value. Positive pre

23、dictive value=80/180=44%Negative predictive value=800/820=98%Test resultDisease No diseaseTotal Positive 80100180Negative 20800820Total 1009001000Predictive value is affected by-Prevalence of the disease in the population:The specificity of the test:Relation of predictive value to disease prevalence

24、Disease prevalenceTest resultsickNot sicktotalsPredictive value1%P9949559499/594=17%N194059406Totals 1009900100005%P495475970495/970=51%N590259030Totals 500950010000Discussion In the example, although the sensitivity and specificity are same, but with the prevalence of the disease in population incr

25、eased, the positive predictive value is increased. It told us: a screening test is most productive and efficient if it is directed to a high-risk target population. Screening a total population for a relatively infrequent disease can be very wasteful of resources and may yield few previously undetec

26、ted cases for the amount of effort involved.Relation of predictive value to the specificity of the testWhen a disease is infrequent, with the specificity increase, the positive predictive value is increased.Because the disease is infrequent, so TN is more big, the FP is more less, that is the denomi

27、nator is small, cause the predictive value is big. Relation of predictive value to specificity(suppose prevalence=10%; sensitivity=100%)Specificity Test resultsickNot sicktotalsPredictive value70%P1000270037001000/3700=27%N063006300Totals 100090001000095%P100045014501000/1450=69%N085508500Totals 100

28、0900010000似然比（likelihood ratio）:阳性似然比：真阳性率/假阳性率，同样为检查阳性，患者是非患者的多少倍，表示阳性结果时，患病与不患病机会之比；阴性似然比：假阴性率/真阴性率，同样是阴性检查结果，患者是非患者的多少倍，表示结果阴性时，患病与不患病机会之比；指标描述的是检查项目的收益程度。例P99Conclusion This chapter has discussed the validity of diagnostic and screening tests as measured by their sensitivity and specificity, the

29、ir predictive value, and the reliability or repeatability of these tests. Clearly, regardless of how sensitivity and specificity a test may be, if its results cannot be replicated, the test is of little use. All these characteristics must be borne in mind when evaluating such tests, together with th

30、e purpose for which the test will be used.例1 人群某病患病状况与筛检结果的关系 80 245 730 775 210 810 1020筛检试验 金标准 合计 患者非患者阳性ABR1阴性CDR2合计C1C2N灵敏度78.6特异度90.1假阳性率9.9假阴性率21.4一致率87.7正确指数0.69阴性似然比0.24阳性似然比7.94阴性预测值94.2阳性预测值67.3Kappa=0.65预测值与受检人群目标疾病患病率的关系 阳性预测值、阴性预测值与患病率、灵敏度和特异度的关系，根据Bayes定理可用以下公式图76 预测值与患病率、灵敏度和特异度的关系较粗

31、的曲线代表灵敏度和特异度均为95%时的预测值较细的曲线代表灵敏度和特异度均为85%时的预测值图中实线为阳性预测值曲线虚线为阴性预测值曲线筛检试验阳性结果截断值的确定 理想的筛检试验 灵敏度、特异度均应接近100%。 但在实际工作中很难达到，往往表现为灵敏度则特异度。 两者高低的转换与确定筛检试验阳性结果的截断值(cut off point)或临界点的选择密切相关。 糖尿病血糖试验的ROC曲线横轴表示假阳性率(1-特异度)纵轴表示真阳性率(灵敏度)点代表筛检试验的特定阳性标准值相对应的灵敏度和特异度对子糖尿病血糖试验不同血糖水平的灵敏度和特异度分布餐后2小时血糖mg/100ml）灵敏度（）特异度（）7098.68.88097.125.59094.347.610088.669.811085.784.112071.492.513064.396.914057.199.415050.099.616047.199.817042.9100.018038.6100.019034.3100.020027.1100.0 ROC曲线也可用来比较两种和两种以上诊断试验的诊断价值，从而帮助临床医师作出最